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def treatoutliers(self, df=None, columns=None, factor=1.5, method='IQR', treament='cap'): | |
""" | |
Removes the rows from self.df whose value does not lies in the specified standard deviation | |
:param columns: | |
:param in_stddev: | |
:return: | |
""" | |
# if not columns: | |
# columns = self.mandatory_cols_ + self.optional_cols_ + [self.target_col] | |
if not columns: |
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Add-WindowsCapability -Online -Name OpenSSH.Client~~~~0.0.1.0 | |
Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0 | |
Start-Service sshd | |
# OPTIONAL but recommended: | |
Set-Service -Name sshd -StartupType 'Automatic' | |
# Confirm the Firewall rule is configured. It should be created automatically by setup. | |
Get-NetFirewallRule -Name *ssh* | |
# There should be a firewall rule named "OpenSSH-Server-In-TCP", which should be enabled | |
# If the firewall does not exist, create one | |
New-NetFirewallRule -Name sshd -DisplayName 'OpenSSH Server (sshd)' -Enabled True -Direction Inbound -Protocol TCP -Action Allow -LocalPort 22 |
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import os | |
import subprocess | |
import shlex | |
import time | |
def convert_doc_to_pdf(ipfile_path, opfile_path): | |
subprocess.Popen(shlex.split('libreoffice --headless --convert-to pdf "{}" --outdir "{}"'.format(ipfile_path, opfile_path)), stdout=True) |
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import pandas as pd | |
import numpy as np | |
def calibration_error(y_true, y_prob, n_bins=5, strategy='uniform', return_expected_caliberation_error=True): | |
if strategy == 'quantile': # Determine bin edges by distribution of data | |
quantiles = np.linspace(0, 1, n_bins + 1) | |
bins = np.percentile(y_prob, quantiles * 100) | |
bins[-1] = bins[-1] + 1e-8 | |
elif strategy == 'uniform': | |
bins = np.linspace(0., 1. + 1e-8, n_bins + 1) |
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import numpy as np | |
import pandas as pd | |
import statsmodels.api as sm | |
transformations = {'log': np.log, | |
'sqrt': np.sqrt, | |
'sqr': lambda x: np.power(x, 2), | |
'cube': lambda x: np.power(x, 3), | |
'cubert': lambda x: np.power(x, -3), | |
'original': lambda x: x} |
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import pandas as pd | |
import numpy as np | |
def closest_node(node, nodes): | |
nodes = np.asarray(nodes) | |
dist_2 = np.sum((nodes - node)**2, axis=1) | |
return np.argmin(dist_2) | |
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from bs4 import BeautifulSoup | |
import pandas as pd | |
soup = BeautifulSoup(open('00001.xml'), 'xml') | |
objs = soup.find_all('object') | |
data = [] | |
for obj in objs: | |
x = [x.get_text() for x in obj.find_all('x')] | |
y = [y.get_text() for y in obj.find_all('y')] | |
name = obj.find('name').get_text() |
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# please install shapely first by `pip install Shapely` | |
from shapely.geometry import Point | |
from shapely.geometry.polygon import Polygon | |
point = Point(0.5, 0.5) | |
polygon = Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]) | |
# this return a bool | |
print(polygon.contains(point)) |
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import datetime | |
cf_history = [('CF_B_HST_MONTH_24', 'CF_B_HST_OS_AMOUNT_24', 'CF_B_HST_AC_DPD_24'), | |
('CF_B_HST_MONTH_23', 'CF_B_HST_OS_AMOUNT_23', 'CF_B_HST_AC_DPD_23'), | |
('CF_B_HST_MONTH_22', 'CF_B_HST_OS_AMOUNT_22', 'CF_B_HST_AC_DPD_22'), | |
('CF_B_HST_MONTH_21', 'CF_B_HST_OS_AMOUNT_21', 'CF_B_HST_AC_DPD_21'), | |
('CF_B_HST_MONTH_20', 'CF_B_HST_OS_AMOUNT_20', 'CF_B_HST_AC_DPD_20'), | |
('CF_B_HST_MONTH_19', 'CF_B_HST_OS_AMOUNT_19', 'CF_B_HST_AC_DPD_19'), | |
('CF_B_HST_MONTH_18', 'CF_B_HST_OS_AMOUNT_18', 'CF_B_HST_AC_DPD_18'), | |
('CF_B_HST_MONTH_17', 'CF_B_HST_OS_AMOUNT_17', 'CF_B_HST_AC_DPD_17'), |
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import re | |
def get_buckets(statuses): | |
status_bucket = [] | |
for status in statuses: | |
dpd = re.search('(\d+) (or above )?day', status, re.IGNORECASE) | |
if dpd: | |
dpd = int(dpd.groups()[0]) | |
if dpd <= 30: | |
status_bucket.append('SMA-0') | |
elif dpd <= 60: |
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